﻿// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "ocr_rec.h"

using namespace cv;

namespace PaddleOCR
{
    CRNNRecognizer::CRNNRecognizer()
    {
    }

    void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
                             std::vector<std::string> &rec_texts,
                             std::vector<float> &rec_text_scores,
                             std::vector<double> &times)
    {

        rec_texts.resize(img_list.size());
        rec_text_scores.resize(img_list.size());

        std::chrono::duration<float> preprocess_diff = std::chrono::duration<float>::zero();
        std::chrono::duration<float> inference_diff = std::chrono::duration<float>::zero();
        std::chrono::duration<float> postprocess_diff = std::chrono::duration<float>::zero();

        int img_num = img_list.size();
        std::vector<float> width_list;
        for (int i = 0; i < img_num; i++)
        {
            width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
        }
        std::vector<int> indices = Utility::argsort(width_list);

        rec_batch_num_ = 1;
        for (int beg_img_no = 0; beg_img_no < img_num;
             beg_img_no += this->rec_batch_num_)
        {
            auto preprocess_start = std::chrono::steady_clock::now();
            int end_img_no = std::min(img_num, beg_img_no + this->rec_batch_num_);
            int batch_num = end_img_no - beg_img_no;
            int imgH = this->rec_image_shape_[1];
            int imgW = this->rec_image_shape_[2];
            float max_wh_ratio = imgW * 1.0 / imgH;
            for (int ino = beg_img_no; ino < end_img_no; ino++)
            {
                int h = img_list[indices[ino]].rows;
                int w = img_list[indices[ino]].cols;
                float wh_ratio = w * 1.0 / h;
                max_wh_ratio = std::max(max_wh_ratio, wh_ratio);
            }

            int batch_width = imgW;
            std::vector<cv::Mat> norm_img_batch;
            for (int ino = beg_img_no; ino < end_img_no; ino++)
            {
                cv::Mat srcimg;
                img_list[indices[ino]].copyTo(srcimg);
                cv::Mat resize_img;
                this->resize_op_.Run(srcimg, resize_img, max_wh_ratio,
                                     this->use_tensorrt_, this->rec_image_shape_);
                this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
                                        this->is_scale_);
                norm_img_batch.push_back(resize_img);
                batch_width = std::max(resize_img.cols, batch_width);
            }

            std::vector<float> input(batch_num * 3 * imgH * batch_width, 0.0f);
            this->permute_op_.Run(norm_img_batch, input.data());
            auto preprocess_end = std::chrono::steady_clock::now();
            preprocess_diff += preprocess_end - preprocess_start;
            
            // Inference.
            auto inference_start = std::chrono::steady_clock::now();
            
            std::vector<int> blob_shape = { batch_num, 3, imgH, batch_width };
            Mat blob(blob_shape, CV_32F, input.data());

            std::vector<Mat> outputs;
            outputs.push_back(predictor_.forward(blob));

            std::vector<int> predict_shape;
            for (int i = 0; i < outputs[0].dims; ++i)
                predict_shape.push_back(outputs[0].size[i]);
            float *predict_batch = (float *)outputs[0].data;

            auto inference_end = std::chrono::steady_clock::now();
            inference_diff += inference_end - inference_start;
            // ctc decode
            auto postprocess_start = std::chrono::steady_clock::now();
            for (int m = 0; m < predict_shape[0]; m++)
            {
                std::string str_res;
                int argmax_idx;
                int last_index = 0;
                float score = 0.f;
                int count = 0;
                float max_value = 0.0f;

                for (int n = 0; n < predict_shape[1]; n++)
                {
                    // get idx
                    argmax_idx = int(Utility::argmax(
                        &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
                        &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
                    // get score
                    max_value = float(*std::max_element(
                        &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
                        &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));

                    if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index)))
                    {
                        score += max_value;
                        count += 1;
                        str_res += label_list_[argmax_idx];
                    }
                    last_index = argmax_idx;
                }
                score /= count;
                if (std::isnan(score))
                {
                    continue;
                }
                rec_texts[indices[beg_img_no + m]] = str_res;
                rec_text_scores[indices[beg_img_no + m]] = score;
            }
            auto postprocess_end = std::chrono::steady_clock::now();
            postprocess_diff += postprocess_end - postprocess_start;
        }
        times.push_back(double(preprocess_diff.count() * 1000));
        times.push_back(double(inference_diff.count() * 1000));
        times.push_back(double(postprocess_diff.count() * 1000));
    }

    void CRNNRecognizer::LoadLabel(const std::string &label_path)
    {
        this->label_list_ = Utility::ReadDict(label_path);
        this->label_list_.insert(this->label_list_.begin(),
            "#"); // blank char for ctc
        this->label_list_.push_back(" ");
    }

    void CRNNRecognizer::LoadModel(const std::string &model_dir)
    {
        predictor_.loadModel(model_dir.c_str());
    }

} // namespace PaddleOCR
